Ventricular Fibrillation Waveform Analysis Research Articles

C6 VENTRICULAR FIBRILLATION SPECTRAL AREA (AMSA) AND LOW–ENERGY SHOCK SUCCESS PREDICTION IN PATIENTS WITH OUT–OF–HOSPITAL CARDIAC ARREST

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy level for defibrillation is that which achieves defibrillation and minimize the current–induced myocardial damage. Therefore, it would be reasonable to reduce the energy level as well as the number of shocks. ECG–based VF waveform analysis features such as amplitude spectral area (AMSA) have been recently introduced as predictors of shock success but their predictivity for shock success with low energy level is not known. We aimed to assess whether AMSA of VF is able to predict the efficacy of low energy level for defibrillation in out–of–hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm occurred from January 2015 to December 2020 in the province of Pavia, Italy, were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2–second–pre–shock ECG interval. Results Among 4619 OHCA, AMSA values and energy for defibrillation were documented in 791 shocks, of which 45% received a shock at low energy (<= 150J) and 55% at high energy (>150J). The rate of efficacy between the two groups did not differ significantly (44% vs 38%, p=0.102), however in patients efficaciously treated with low energy, AMSA was higher compared to those efficaciously treated with high energy [13.2 mV·Hz (12.5–14.2) vs 10.8 (10.1–11.5), p<0.001]. Moreover, AMSA was found to be different even when comparing ineffective shock at low energy with effective shock at high energy [(6.6 (4.6–10) vs 10.8 (8.1–13.8), p<0.001] and similar when comparing ineffective shock at low and at high energy [6.6 (4.6–10) vs 6.3 (4.5–8.7), p=0.21]. By dividing AMSA values into three tertiles the rate of shock success at low energy was found to be different: [T1 (0.7–6.2) 4.2%; T2 (6.2–10.8) 13%; T3 (10.8–63.2) 42%, Chi squared p<0.001 and p for trend <0.001]. After correction for age, sex, amiodarone use, call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success compared with the values in the lowest tertile [T3 OR 15 (95%CI 7–30), p< 0.001; T2 OR 3 (95%CI 1–7), p= 0.002]. Conclusion Amplitude spectral area of VF is a predictor of shock success at low energy. This could be useful to optimize the choice of energy limiting the current related myocardial injury.

1105 VENTRICULAR FIBRILLATION SPECTRAL AREA (AMSA) AND LOW-ENERGY SHOCK SUCCESS PREDICTION IN PATIENTS WITH OUT-OF-HOSPITAL CARDIAC ARREST

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy level for defibrillation is that which achieves defibrillation and minimize the current-induced myocardial damage. Therefore, it would be reasonable to reduce the energy level as well as the number of shocks. ECG-based VF waveform analysis features such as amplitude spectral area (AMSA) have been recently introduced as predictors of shock success but their predictivity for shock success with low energy level is not known. We aimed to assess whether AMSA of VF is able to predict the efficacy of low energy level for defibrillation in out-of-hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm occurred from January 2015 to December 2020 in the province of Pavia, Italy, were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2-second-pre-shock ECG interval. Results Among 4619 OHCA, AMSA values and energy for defibrillation were documented in 791 shocks, of which 45% received a shock at low energy (<= 150J) and 55% at high energy (>150J). The rate of efficacy between the two groups did not differ significantly (44% vs 38%, p=0.102), however in patients efficaciously treated with low energy, AMSA was higher compared to those efficaciously treated with high energy [13.2 mV·Hz (12.5-14.2) vs 10.8 (10.1-11.5), p<0.001]. Moreover, AMSA was found to be different even when comparing ineffective shock at low energy with effective shock at high energy [(6.6 (4.6-10) vs 10.8 (8.1-13.8), p<0.001] and similar when comparing ineffective shock at low and at high energy [6.6 (4.6-10) vs 6.3 (4.5-8.7), p=0.21]. By dividing AMSA values into three tertiles the rate of shock success at low energy was found to be different: [T1 (0.7-6.2) 4.2%; T2 (6.2-10.8) 13%; T3 (10.8-63.2) 42%, Chi squared p<0.001 and p for trend <0.001]. After correction for age, sex, amiodarone use, call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success compared with the values in the lowest tertile [T3 OR 15 (95%CI 7-30), p< 0.001; T2 OR 3 (95%CI 1-7), p= 0.002]. Conclusion Amplitude spectral area (AMSA) of VF is a predictor of shock success at low energy. This could be useful to optimize the choice of energy limiting the current related myocardial injury.

Ventricular fibrillation amplitude spectral area as a guide to deliver the optimal energy level for defibrillation

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy should be the lowest energy effective to achieve defibrillation minimizing the current-induced myocardial damage. Therefore, it would be ideal to minimize the energy level as well as the number of shocks during resuscitation. ECG-based VF waveform analysis features such as amplitude spectral area have been recently introduced as predictors of shock success, but their predictivity for shock success with low energy level is not known. Purpose To assess whether amplitude spectral area (AMSA) of VF is able to predict the efficacy of low energy level defibrillation in out-of-hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm that occurred from January 2015 to December 2020 were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2-second-pre-shock ECG interval. Results Among 4619 OHCAs, resuscitation was attempted in 2982 (64%) and at least one shock was delivered in 697 (15%). AMSA values and defibrillation energy were available for 791 shocks, of which 45% received shock at low energy (>150J) and 55% at high energy (>150J). The rate of efficacy between the two groups was similar (44% vs 38%, p=0.102). However, in patients efficaciously treated with shock at low energy, AMSA was higher compared to those treated with shock at high energy [13.2 mV Hz (IQR 10.2–17) vs 10.8 mV Hz (IQR 8–13.8), p<0.001]. Moreover, AMSA values were significantly different when comparing ineffective shock at low energy with effective shock at high energy [6.6 mV Hz (IQR 4.6–10) vs 10.8 mV Hz (IQR 8.1–13.8), p<0.001] and similar when comparing ineffective shock at low and high energy [6.6 mV Hz (IQR 4.6–10) vs 6.3 mV Hz (IQR 4.5–8.7), p=0.21]. By dividing AMSA values into three tertiles, the rate of shock success at low energy was statistically different: [T1 (0.7–6.2 mV Hz) 4.2%; T2 (6.2–10.8 mV Hz) 13%; T3 (10.8–63.2 mV Hz) 42%; Chi-squared p<0.001 and p for trend <0.001]. After correction for age, sex, amiodarone use and call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success at low energy compared to the lowest tertile [T3 OR 15 (95% CI 7–30), p<0.001; T2 OR 3 (95% CI 1–7), p=0.002]. Conclusion Ventricular fibrillation amplitude spectral area is a predictor of shock success at a low energy level. This could be useful to optimize both time and dose-energy to patients, yielding the highest chance for successful defibrillation while reducing the number of futile shocks and thus limiting the total current myocardial energy as well as CPR interruptions. Funding Acknowledgement Type of funding sources: None.

Optimizing defibrillation during cardiac arrest

Current cardiac arrest guidelines are based on a fixed, time-based defibrillation strategy. Rhythm analysis and shock delivery (if indicated) are repeated every 2 min requiring cyclical interruptions of chest compressions. This approach has several downsides, such as the need to temporarily stop cardiopulmonary resuscitation (CPR) for a variable amount of time, thus reducing myocardial perfusion and decreasing the chance of successful defibrillation. A tailored defibrillation strategy should identify treatment priority for each patient, that is chest compressions (CCS) or defibrillation, minimize CCs interruptions, speed up the delivery of early effective defibrillation and reduce the number of ineffective shocks. Real-time ECG analysis (using adaptive filters, new algorithms robust to chest compressions artifacts and shock-advisory algorithms) is an effective strategy to correctly identify heart rhythm during CPR and reduce the hands-off time preceding a shock. Similarly, ventricular fibrillation waveform analysis, that is amplitude spectrum area (AMSA) represents a well established approach to reserve defibrillation in patients with high chance of shock success and postpone it when ventricular fibrillation termination is unlikely. Both approaches demonstrated valuable results in improving cardiac arrest outcomes in experimental and observational study. Real-time ECG analysis and AMSA have the potential to predict ventricular fibrillation termination, return of spontaneous circulation and even survival, with discretely high confidence. Prospective studies are now necessary to validate these new approaches in the clinical scenario.

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest.

BackgroundIn cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in‐human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in‐field studies on acute MI.Methods and ResultsWe conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010–2014). From 12‐lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12‐lead, AMSA only; and model C, 12‐lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C‐statistic of 0.61 (95% CI, 0.54–0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59–0.73), P=0.09 versus AMSA lead II. Model B yielded a higher C‐statistic: 0.75 (95% CI, 0.68–0.81), P<0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67–0.80), P=0.66 versus model B.ConclusionsThis proof‐of‐concept study provides the first in‐human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in‐field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.

Validation of spectral energy for the quantitative analysis of ventricular fibrillation waveform to guide defibrillation in a porcine model of cardiac arrest and resuscitation.

The amplitude spectrum area (AMSA), a frequency-domain ventricular fibrillation (VF) waveform metric, can predict successful defibrillation and the return of spontaneous circulation (ROSC) after defibrillation attempts. We aimed to investigate the validation of Spectral Energy for the quantitative analysis of the VF waveform to guide defibrillation in a porcine model of cardiac arrest and compare it with the AMSA metric. In addition, we sought to determine the effects of epinephrine and cardiopulmonary resuscitation (CPR) on AMSA and Spectral Energy. Sixty male domestic pigs weighing 35 to 45 kg were involved in this study. VF was initially untreated for 10 min followed by 6 min of CPR. Epinephrine was administered to the animals after 2 min of CPR. After the CPR, a single 120-J biphasic shock was applied to the animals. AMSA and Spectral Energy values were measured every minute from the electrocardiogram (ECG) to defibrillation. Receiver operating characteristic (ROC) curves were calculated for both the Spectral Energy and AMSA methods. Spectral Energy and AMSA values gradually decayed during untreated VF in all the animals. However, after the application of CPR and epinephrine, Spectral Energy and AMSA values were significantly increased in animals which were later successfully defibrillated, but did not increase in animals in which defibrillation was unsuccessful. The ROC curves showed that the Spectral Energy and AMSA methods possessed similar levels of sensitivity and specificity in predicting defibrillation success (P<0.001). Both the Spectral Energy and AMSA methods accurately predict successful defibrillation. Moreover, increases in the value of either Spectral Energy or AMSA after application of CPR and epinephrine may also predict successful defibrillation.

Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis?

BackgroundQuantitative analysis of ventricular fibrillation (VF), such as amplitude spectral area (AMSA), predicts shock outcomes. However, there is no uniform definition of shock/cardiopulmonary resuscitation (CPR) success in out-of-hospital cardiac arrest (OHCA). The objective of this study is to investigate post-shock rhythm variations and the impact of shock/CPR success definition on the predictability of AMSA. MethodsA total of 554 shocks from 257 OHCA patients with VF as initial rhythm were analyzed. Post-shock rhythms were analyzed every 5s up to 120s and annotated as VF, asystole (AS) and organized rhythm (OR) at serial time intervals. Three shock/CPR success definitions were used to evaluate the predictability of AMSA: (1) termination of VF (ToVF); (2) return of organized electrical activity (ROEA); (3) return of potentially perfusing rhythm (RPPR). ResultsRhythm changes occurred after 54.5% (N=302) of shocks and 85.8% (N=259) of them occurred within 60s after shock delivery. The observed post-shock rhythm changes were (1) from AS to VF (24.9%), (2) from OR to VF (16.1%), and (3) from AS to OR (12.1%). The area under the receiver operating characteristic curve (AUC) for AMSA as a predictor of shock/CPR success reached its maximum 60s post-shock. The AUC was 0.646 for ToVF, 0.782 for ROEA, and 0.835 for RPPR (p<0.001) respectively. ConclusionsPost-shock rhythm is unstable in the first minute after the shock. The predictability of AMSA varies depending on the definition of shock/CPR success and performs best with the return of potentially perfusing rhythm endpoint for OHCA.

Abstract 12825: Feasibility of Amplitude Spectrum Area Estimation in Ventilation Pauses During Cardiopulmonary Resuscitation

Introduction: Amplitude spectral area (AMSA) calculated from ventricular fibrillation (VF) waveform not only predicts defibrillation outcome, but may also reflect effectiveness of chest compressions. However, reliable VF waveform analysis usually requires rescuers to stop compressions. In the present study, we investigated whether AMSA could be accurately estimated in ventilation pauses during cardiopulmonary resuscitation (CPR). Methods: A total of 40 out-of-hospital cardiac arrest patients with VF as the initial rhythm were included in this study. All patients received resuscitation with a 30:2 compressions to ventilation ratio. ECG data and chest compression waveforms before first defibrillation were extracted. Each episode included at least one full resuscitation cycle consisting of 30 compressions followed by a ventilation pause, further compressions, and a pre-shock pause with artifact-free VF signal. AMSA was continuously calculated using a sliding window of 2 seconds duration with sliding step size of 1 sampling point. Ventilation pauses were identified by the chest compression waveforms. Mean AMSA value during chest compression (AMSA_c) and minimal AMSA value during ventilation pause (AMSA_p) were compared with the AMSA value measured in noise-free VF (AMSA_nf) during the pre-shock pause. Results: Twenty patients received mechanical CPR and other 20 patients received manual chest compressions. The ventilation pause was 3.19±0.62 sec for mechanical CPR and 5.96±2.06 for manual CPR. AMSA_c was significantly higher than AMSA_p (32.4±30.0 mVHz vs. 6.6±3.9 mVHz, p &lt;0.01) and AMSA_nf (32.4±30.0 mVHz vs. 6.0±3.5 mVHz, p &lt;0.01). As shown in Figure 1, AMSA_nf was strongly correlated with AMSA_p (r=0.90, p &lt;0.01) but not with AMSA_c (r=0.50, p =0.11). Conclusions: AMSA could be reliably determined in ventilation pauses during CPR but not during chest compressions.

Waveform Analysis–Guided Treatment Versus a Standard Shock-First Protocol for the Treatment of Out-of-Hospital Cardiac Arrest Presenting in Ventricular Fibrillation

Background— Ventricular fibrillation (VF) waveform properties have been shown to predict defibrillation success and outcomes among patients treated with immediate defibrillation. We postulated that a waveform analysis algorithm could be used to identify VF unlikely to respond to immediate defibrillation, allowing selective initial treatment with cardiopulmonary resuscitation in an effort to improve overall survival. Methods and Results— In a multicenter, double-blind, randomized study, out-of-hospital cardiac arrest patients in 2 urban emergency medical services systems were treated with automated external defibrillators using either a VF waveform analysis algorithm or the standard shock-first protocol. The VF waveform analysis used a predefined threshold value below which return of spontaneous circulation (ROSC) was unlikely with immediate defibrillation, allowing selective treatment with a 2-minute interval of cardiopulmonary resuscitation before initial defibrillation. The primary end point was survival to hospital discharge. Secondary end points included ROSC, sustained ROSC, and survival to hospital admission. Of 6738 patients enrolled, 987 patients with VF of primary cardiac origin were included in the primary analysis. No immediate or long-term survival benefit was noted for either treatment algorithm (ROSC, 42.5% versus 41.2%, P =0.70; sustained ROSC, 32.4% versus 33.4%, P =0.79; survival to admission, 34.1% versus 36.4%, P =0.46; survival to hospital discharge, 15.6% versus 17.2%, P =0.55, respectively). Conclusions— Use of a waveform analysis algorithm to guide the initial treatment of out-of-hospital cardiac arrest patients presenting in VF did not improve overall survival compared with a standard shock-first protocol. Further study is recommended to examine the role of waveform analysis for the guided management of VF. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT00535106.

Prediction of Defibrillation Outcome by Ventricular Fibrillation Waveform Analysis: A Clinical Review

The most frequent initial rhythm in out-of-hospital witnessed cardiac arrest is ventricular fibrillation (VF) and electrical defibrillation is still the only effective therapy for the termination of this life-threatening cardiac arrhythmia. Even though earlier defibrillation is greatly emphasized during cardiopulmonary resuscitation (CPR), unnecessary or repetitive high energy defibrillations are associated with decreased post-resuscitation myocardial function. Optimizing the timing of defibrillation is of great importance in order to discriminate patients should receive immediate defibrillation versus alternate therapies such as CPR. Since characteristics of VF waveform changes over time and with CPR, which exhibit predictable ability of defibrillation success, quantitative analysis of VF waveform has the potential to guide defibrillation. This article reviewed methods developed for VF waveform analysis (including time domain, frequency domain, time-frequency domain, nonlinear analysis, and combination analysis techniques) and their performances for the prediction of defibrillation outcomes in clinical settings. The retrospective meta-analysis confirmed that VF waveform could predict the return of organized electrical activity, restoration of spontaneous circulation, and survival reliably. Additionally, predictors based on time-frequency and nonlinear methods were superior to other methods on the whole. However, no prospective studies have been performed to identify the optimal time of defibrillation utilizing VF waveform analysis until now. Therefore, the value of VF waveform analysis to guide clinical countershock management still needs further investigation.

Abstract 303: End-Tidal CO2 Reactivity Is Associated with Defibrillation Success

Background: High quality cardiopulmonary resuscitation (CPR) appears to modulate ventricular fibrillation (VF) morphology and increase defibrillation success. However, VF waveform analysis has not proven to be clinically effective in guiding resuscitation. End-tidal CO2 (PetCO2) correlates well with cardiac perfusion during CPR. Objective: To evaluate PetCO2 as a predictor of defibrillation success. Methods: This was a prospective, observational study conducted at two urban academic hospitals with a combined total of 500 beds. Data regarding defibrillation attempts, CPR process, and PetCO2 were abstracted from monitor/defibrillators. All arrest victims with primary VF or ventricular tachycardia (VT) for whom CPR and PetCO2 data were available were included in this analysis. Patients with return of spontaneous circulation (ROSC) were compared to those without ROSC with regard to the change in PetCO2, mean and final compression rate and depth, and chest compression fraction (CCF). Results: A total of 32 patients were included in this analysis; 19 of these (59%) had ROSC. PetCO2 values prior to the final defibrillation attempt were 121% higher than initial PetCO2 values in patients with ROSC versus only 19% higher in patients without ROSC (p&lt;0.01). Patients with and without ROSC were similar with regard to mean compression depth (2.5 vs. 2.7 inches, p=NS), final compression depth (2.7 vs. 2.8 inches, p=NS), mean compression rate (119/min for both, p=NS), final compression rate (121/min vs. 123/min, p=NS), mean CCF (83% vs. 85%, p=NS), and final CCF (83% vs. 91%). Conclusions: An increase in PetCO2 was associated with ROSC among inpatients with VF/VT arrest. No differences with regard to overall or final CPR process measures were observed. The “PetCO2 responsiveness” to CPR may reflect an underlying physiology that predicts defibrillation success.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

BackgroundVentricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique.MethodsA total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold.ResultsThe integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively.ConclusionWe report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.

Part 6: Defibrillation

Part 6: Defibrillation

Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest

Aims Repeated failed shocks for ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA) can worsen the outcome. It is very important to rapidly distinguish between early and late VF. We hypothesised that VF waveform analysis based on detrended fluctuation analysis (DFA) can help predict successful defibrillation. Methods Electrocardiogram (ECG) recordings of VF signals from automated external defibrillators (AEDs) were obtained for subjects with OOHCA in Taipei city. To examine the time effect on DFA, we also analysed VF signals in subjects who experienced sudden cardiac death during Holter study from PhysioNet, a publicly accessible database. Waveform parameters including root-mean-squared (RMS) amplitude, mean amplitude, amplitude spectrum analysis (AMSA), frequency analysis as well as fractal measurements including scaling exponent (SE) and DFA were calculated. A defibrillation was regarded as successful when VF was converted to an organised rhythm within 5 s after each defibrillation. Results A total of 155 OOHCA subjects (37 successful and 118 unsuccessful defibrillations) with VF were included for analysis. Among the VF waveform parameters, only AMSA (7.61 ± 3.30 vs. 6.30 ± 3.13, P = 0.028) and DFAα2 (0.38 ± 0.24 vs. 0.49 ± 0.24, P = 0.013) showed significant difference between subjects with successful and unsuccessful defibrillation. The area under the curves (AUCs) for AMSA and DFAα2 was 0.63 (95% confidence interval (CI) = 0.52–0.73) and 0.65 (95% CI = 0.54–0.75), respectively. Among the waveform parameters, only DFAα2, SE and dominant frequency showed significant time effect. Conclusions The VF waveform analysis based on DFA could help predict first-shock defibrillation success in patients with OOHCA. The clinical utility of the approach deserves further investigation.

Ventricular Fibrillation Waveform Analysis during Cardiopulmonary Resuscitation

Ventricular fibrillation (VF) is the primary rhythm associated with cardiac arrest characterized as rapid, disorganized contrac-tions of the heart with complex electrocardiogram (ECG) patterns. Recent studies have reported that performing cardiopul-monary resuscitation (CPR) procedure prior to shock increases the survival rate especially especially when VF is untreated for more than 5 minutes. The waveform analysis is objective help in the choice of the right therapy (shock parameters, shock first or CPR first, drug administration). This analysis is a precondition of individually optimized defibrillation and contribute substantially to an increased quality of CPR and reduce delivery of failed rescue shock. Animal and clinical studies con-firmed that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate and further improved countershock outcome prediction.

Predicting defibrillation success

Ventricular fibrillation is the primary rhythm in many cardiac arrest patients. Since the late 1980s, the surface electrocardiogram of ventricular fibrillation has been subjected to analysis to obtain reliable information about the likelihood of successful countershock and to estimate the duration of cardiac arrest. Considerable efforts were made in the past 2 years to further improve the predictive power of rescue shock measures. In a retrospective clinical study, ventricular fibrillation single feature analysis was not able to reliably estimate duration between cardiac arrest onset and initial electrocardiogram. Combining ventricular fibrillation features in the time and frequency domain by employing neural networks did not further improve the best single feature prediction power taken from higher ventricular fibrillation frequency bands. Cardioversion outcome prediction based on the wavelet technique increased the specificity up to 66% at the 95% sensitivity level. Recent results question the ventricular fibrillation feature analysis as a reliable tool to estimate the duration of human cardiac arrest. Animal and clinical studies confirmed that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate and further improved countershock outcome prediction. Prospective clinical studies are highly warranted to demonstrate that ventricular fibrillation waveform analysis definitely improves survival after cardiac arrest.

Scientific Knowledge Gaps and Clinical Research Priorities for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Identified During the 2005 International Consensus Conference on ECC and CPR Science With Treatment Recommendations

Scientific Knowledge Gaps and Clinical Research Priorities for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Identified During the 2005 International Consensus Conference on ECC and CPR Science With Treatment Recommendations

Is ventricular fibrillation waveform analysis suitable for optimizing timing of ventricular defibrillation? Yes it is

Is ventricular fibrillation waveform analysis suitable for optimizing timing of ventricular defibrillation? Yes it is

Is ventricular fibrillation waveform analysis suitable for optimizing timing of ventricular defibrillation? Yes it is

Weil Institute of Critical Care Medicine, Rancho Mirage, CA, Postgraduate School of Anesthesia and Intensive Care, University of Trieste, Trieste, Italy (Ristagno) Anaesthesia and Intensive Care, University Hospital, Catania, Italy (Gullo)

Monitoring the quality of cardiopulmonary resuscitation

Numerous recent reports have described limitations in the quality of cardiopulmonary resuscitation. Thus, there has been increasing interest in the techniques available to monitor quality. This review focuses on the major publications since the review published by the International Liaison Committee on Resuscitation in 2005. Some key articles published prior to this time period have also been included. A number of devices can monitor various components of the quality of cardiopulmonary resuscitation. End-tidal CO2 measurement assists in confirming placement of endotracheal tubes, correlates with cardiac output and detects the return of spontaneous circulation. Turbine flow-meters monitor respiratory rate and tidal volume. Transthoracic impedance monitoring measures respiratory rate, and may assist in confirmation of endotracheal tube placement. A new mechanical device (CPREzy) and a new defibrillator/monitor allow estimation of depth (and rate) of compressions. Ventricular-fibrillation waveform analysis may facilitate better timing of defibrillation. Echocardiography detects conditions that may impair the quality of cardiopulmonary resuscitation. Many options are available to monitor the quality of cardiopulmonary resuscitation. Some have significant limitations, and others are only readily available in hospital. The use of the information from this more intensive monitoring promises to improve outcomes of cardiopulmonary resuscitation.